Innovate UK backs Morgan Sindall AI whole life carbon tool

The UK construction industry is on the cusp of a new era where WLCAs have a much greater weighting. WLCAs combine carbon measures for day-to-day occupancy with those for embodied emissions from materials and energy used to produce and assemble materials in construction and over the life of an asset. They are on the critical path of efforts to achieve Net Zero as around 25% of UK carbon emissions are linked to the built environment and materials such as steel and cement alone account for around 15% of global carbon emissions.

Inputting data and producing high-quality assessments is a time intensive activity. By harnessing AI capability, the new system will accelerate creating WLCAs by as much as 85%. The significant time and carbon savings will in turn pave the way towards much higher levels of WLCA adoption in the sector as a whole and therefore greatly assist in the efforts to the UK’s efforts to achieve carbon reductions.

Innovate UK has made a grant of £947,000 against the three partners’ research and development costs of £1,418,000. The award recognises that innovation is required to support the rapid adoption of WLCAs within the UK construction industry, with governments around the world increasingly mandating their use. It also noted that carbon pricing, which makes emitters pay for the true cost of pollution, is being considered for introduction into the building sector. Its arrival in the UK would further stimulate WLCA demand and introduce commercial incentives to reduce carbon.

Tim Clement, Director of Social Value & Sustainability at Morgan Sindall Construction, said:

We started working on the research and development phase of CarboniCa back in 2017 and the Innovate UK funding award recognises the enormous potential for further development of the tool we have already in service and helping reduce carbon in a transparent, credible way. Innovation in this area has much to contribute in terms of increasing confidence in decision-making around sustainability, something that can only happen with credible data outputs being made more readily available. We have an established and highly effective working relationship with our research and development partners at Nottingham Trent and ConstructSys respectively. Our next step is building a cutting edge AI engine into a state of the art system that drastically reduces the time taken to produce an accurate WLCA. The funding means we can develop a series of new algorithms to exploit the growing dataset of actual project data already inputted into the CarboniCa tool, streamlining the process of creating early-stage benchmarks for different building types. The algorithms will also generate insights based on human-curated product databases, to further the tool’s mission as a carbon reduction tool.”

NTU’s research team operates within the School of Architecture, Design and the Built Environment. It is co-led led by Professor Amin Al-Habaibeh, Professor of Intelligent Engineering systems, and Dr Emmanuel Manu, Associate Professor in Construction Management.

Professor Al-Habaibeh, Director of the Product Innovation Centre in the, said:

This is a great opportunity to implement cutting-edge deep learning AI technology to enhance productivity, improve competitiveness and speed up the evaluation process of building’s carbon footprint. This will enable a better material selection and more optimum designs to address climate change. This collaboration with Morgan Sindall and partner organisations will bridge the gap between academic innovation and implementation and will also benefit NTU’s future AI teaching and training in wide range of disciplines as sustainability is at the heart of everything we do.”

Dr Emmanuel Manu commented:

The construction and operation of buildings contribute to about 33% of greenhouse gas emissions and 40% of global energy consumption. This makes it important to address the whole life cycle of construction projects starting from the design stage to reduce carbon emissions.

AI is a general term for describing when a machine mimics human cognitive functions, like problem-solving, pattern recognition, and learning. Machine learning is a subset of AI and uses statistical techniques to give computer systems the ability to ‘learn’ from data, without being explicitly programmed. A machine becomes better at understanding and providing insights as it is exposed to more data. The advance of these technologies in the construction industry is one of the industry's most compelling trends.

Digital systems such as Building Information Modelling (BIM) have been around for a long time now and, when harnessed effectively, make a hugely positive impact in terms of successfully delivering projects,” added Tim Clement.

When it comes to the creation and adoption of next generation solutions, such as harnessing AI and machine learning, there are even bigger challenges, such as making sure we use the technology in a highly transparent, human-centred way. That’s why the innovation we are applying to here is important and can help set a path to a lower carbon future.”

ENDS

For more information on this press release, contact Chris Hulme on [email protected]